The “Python Performance Optimization and Scaling Strategies” course is designed for equipping participants with advanced techniques and tools for optimizing Python applications. The focus will be on efficient memory management, CPU performance optimization, and efficient scaling to address performance bottlenecks.

Learners will have hands-on experience using Python profiling tools like cProfile, memory_profiler, and Py-Spy and implement strategies for effective network optimization. The course also intends to imbibe into participants performance-oriented coding best practices to successfully implement scalable architectures. Finally, in addition to analyzing and fixing system-level inefficiencies, they will also learn about the best practices for improving REST APIs, asynchronous programming, and using Python libraries NumPy and Cython.

By the end of this course, you will be able to identify and fix performance challenges, get a practical toolkit to address memory leaks, task optimization, and dynamic scaling to meet real-world demands.

Key Takeaways:

• Python profiling tools like cProfile, memory_profiler, and Py-Spy.
• Tools like memory_profiler and Pympler to detect and resolve memory leaks.
• Optimize CPU performance with multithreading, multiprocessing, and asynchronous programming.
• Hands-on experience with Celery for distributed task management
• Using Flask for building scalable microservices.
• Network traffic analysis using Scapy.
• REST API optimization techniques.
• Advanced optimization using NumPy, Cython, and line_profiler.